Provider Comparison

AWS vs Vast.ai

AWS and Vast.ai represent contrasting approaches in the GPU cloud market for ML/AI workloads. AWS, as the market leader, offers a comprehensive, enterprise-grade ecosystem with seamless integration across services like SageMaker, EC2 P5 instances with H100 GPUs, and proprietary chips like Trainium for cost-efficient training. It excels in global scalability, high availability across regions, and robust compliance (SOC 2, HIPAA, GDPR), making it ideal for large enterprises needing reliability and deep tooling integration. However, its pricing complexity, including data egress fees, and higher baseline costs can deter cost-sensitive users. Vast.ai, conversely, operates as a decentralized marketplace connecting users directly to GPU hosts worldwide, prioritizing absolute lowest costs through competitive bidding and spot pricing. It suits independent researchers, startups, and experimenters with features like DLPerf/$ filters for value-optimized searches and support for distributed workloads. While offering GPUs from NVIDIA A100 to H100 at fractions of AWS prices, it trades off consistency, with variable host quality, limited enterprise compliance (GDPR only), and potential downtime from peer-hosted infrastructure. Key differentiators include AWS's managed services and SLAs versus Vast.ai's raw cost savings and flexibility. AWS provides superior value for production-scale, mission-critical AI, while Vast.ai shines for prototyping and budget-constrained exploration, enabling ML engineers to maximize compute dollars amid rising GPU demands.

Visit AWS Visit Vast.ai

Our Recommendation

Choose AWS for production deployments, large teams (50+ engineers), or regulated industries requiring HIPAA/SOC 2 compliance, seamless SageMaker integration, and global redundancy. It's optimal when reliability trumps cost, such as enterprise LLM serving or multi-region training with budgets exceeding $10K/month, leveraging spot instances for 70-90% savings on interruptible jobs. Opt for Vast.ai with small teams (<10), tight budgets (<$5K/month), or experimental workflows tolerant of interruptions. It's ideal for cost-driven fine-tuning, distributed hyperparameter searches, or hobbyist projects, where per-hour rates can undercut AWS by 5-10x. Avoid Vast.ai for latency-sensitive inference or when Kubernetes orchestration and EBS-scale storage are needed. Hybrid approaches—Vast.ai for dev/test, AWS for prod—maximize value for scaling startups.

Live Pricing

Compare real-time GPU offers from AWS and Vast.ai

73 offers available

Provider	GPU Model	VRAM	Host Specs	Region	Price	Status
Vast.ai	8×NVIDIA GeForce RTX 3060 12GB VRAM	12GB	24 vCPU 126GB RAM 738GB Storage	Quebec	$0.00/GPU/hr $0.01/hr total (8×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1660GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	6×NVIDIA GeForce RTX 3080 Ti 12GB VRAM	12GB	8 vCPU 94GB RAM 1527GB Storage	Ukraine	$0.01/GPU/hr $0.04/hr total (6×)	Sold Out
Vast.ai	NVIDIA GeForce RTX 3060 12GB VRAM	12GB	4 vCPU 23GB RAM 670GB Storage	Turkey	$0.01/GPU/hr	Sold Out
Vast.ai	NVIDIA GeForce RTX 3080 10GB VRAM	10GB	24 vCPU 31GB RAM 327GB Storage	Japan	$0.01/GPU/hr	Sold Out

Vast.ai

Quebec

Sold Out

NVIDIA GeForce RTX 30608x

12GB VRAM

24 vCPU

126GB RAM

738GB Storage

625 Mbps ↑

626 Mbps ↓

$0.00/GPU/hr

$0.01/hr total (8×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1660GB Storage

394 Mbps ↑

689 Mbps ↓

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Ukraine

Sold Out

NVIDIA GeForce RTX 3080 Ti6x

12GB VRAM

8 vCPU

94GB RAM

1527GB Storage

$0.01/GPU/hr

$0.04/hr total (6×)

Vast.ai

Turkey

Sold Out

NVIDIA GeForce RTX 3060

12GB VRAM

4 vCPU

23GB RAM

670GB Storage

21 Mbps ↑

99 Mbps ↓

$0.01/GPU/hr

Vast.ai

Japan

Sold Out

NVIDIA GeForce RTX 3080

10GB VRAM

24 vCPU

31GB RAM

327GB Storage

120 Mbps ↑

461 Mbps ↓

$0.01/GPU/hr

View all 73 offers

AWS(Est. 2006)

The dominant force in global cloud computing with deep integration of GPUs into its ecosystem for machine learning and other services.

Best For

Large-scale enterprises requiring deep integration with other cloud servicesOrganizations needing globally redundant availability zones

Unique Features

Proprietary silicon like Trainium and Inferentia chips
Fully managed ML development environment with SageMaker

Limitations

High cost relative to specialized clouds
Complexity of pricing including egress fees

Vast.ai(Est. 2018)

A decentralized marketplace for absolute lowest costs and distributed experiments.

Best For

Absolute lowest costsDistributed experiments

Unique Features

Granular search filters like DLPerf/$
Decentralized marketplace

Feature Comparison

Access Methods

Feature	AWS	Vast.ai
SSH
Jupyter Notebooks
Web Terminal
API
Kubernetes
Containers

Billing Options

Feature	AWS	Vast.ai
Billing Increment	per-second	per-hour
Spot Instances
Reserved Instances
Prepaid Credits

Compliance

Certification	AWS	Vast.ai
SOC 2
HIPAA
GDPR
ISO 27001

Support

Feature	AWS	Vast.ai
SLA
Enterprise Support
Discord Community

Pricing Analysis

Pricing Overview

AWS employs per-second billing on EC2 GPU instances (e.g., g5.xlarge with A10G), enabling precise cost control for short jobs, alongside on-demand, spot (up to 90% off), and reserved/savings plans for long-term commitments. Egress fees ($0.09/GB) and complex tiered pricing add overhead. Vast.ai uses per-hour billing via its marketplace, with real-time bidding for on-demand or spot rentals, no egress fees, but minimum 1-hour charges inflate small-job costs. Spot availability fluctuates with host supply, lacking AWS's predictive capacity. Implications: AWS favors bursty, variable workloads; Vast.ai suits steady, long-running jobs where hourly rates yield 3-10x savings, though budgeting requires monitoring marketplace volatility.

Value Assessment

Vast.ai delivers superior value for small experiments and fine-tuning (e.g., <24h A100 rentals at $0.50-1/hr vs AWS $3-5/hr), maximizing DLPerf/$ for budget-conscious prototyping. For large training runs (>1 week), AWS spot instances on P4d/P5 clusters offer better effective value with reliable scaling and no host variability. Production inference favors AWS for consistent low-latency via Inferentia/Trainium at scale. Batch inference leans Vast.ai for cost if deadlines flex; AWS wins for integration. Overall, Vast.ai for <10% AWS spend tolerance; AWS for enterprise predictability.

Use Case Comparison

LLM Training

AWS recommended

AWS

AWS excels with P5 instances (8x H100s, NVLink), SageMaker for managed distributed training via Ray/SMX, and Trainium for 40-50% cost savings on FP16. Global AZs ensure high availability; spot fleets handle petabyte-scale datasets reliably. Ideal for teams needing orchestration, monitoring, and compliance.

Vast.ai

Vast.ai offers H100 clusters at 4-8x lower rates, suitable for cost-sensitive large-model pretraining if interruptions are tolerable. Marketplace filters enable multi-node scaling, but variable interconnects (InfiniBand/Ethernet) and host reliability demand custom fault-tolerance scripting.

Batch Inference

Vast.ai recommended

AWS

AWS SageMaker Batch Transform and Inferentia (Inf2 instances) provide optimized, serverless scaling for high-throughput jobs, with S3 integration and auto-scaling. Cost-effective via spot, but egress adds ~10% overhead for large outputs.

Vast.ai

Vast.ai shines for cheap, high-volume batch jobs on A100/H100s (e.g., $0.40/hr), with easy Docker uploads. Lacks managed queuing; users handle parallelism, but no egress fees boost value for data-heavy workflows.

Real-time Inference

AWS recommended

AWS

AWS leads with low-latency SageMaker Endpoints, Lambda@Edge, and Inferentia for <100ms p99 on global traffic. ECS/EKS support autoscaling; API Gateway integration ensures enterprise-grade SLAs and monitoring.

Vast.ai

Vast.ai viable for dev/testing with FastAPI on GPUs, but inconsistent networking (1-10Gbps) and no load-balancing suit non-prod only. Downtime risks make it unsuitable for customer-facing latency SLAs.

Fine-tuning & Experimentation

Vast.ai recommended

AWS

AWS SageMaker Studio notebooks and JumpStart models accelerate iteration, with spot for cheap trials. However, higher costs limit hyperparameter sweeps compared to spot-market alternatives.

Vast.ai

Vast.ai dominates with granular DLPerf/$ searches, instant A100 access at $0.30-0.80/hr, and easy multi-config spins. Perfect for rapid, low-budget experiments; spot bidding optimizes for 100s of short runs.

Technical Comparison

Infrastructure

AWS provides virtualized EC2 instances with EBS/EFS storage (up to 48TB NVMe), Elastic Fabric Adapter (400Gbps networking), and full EKS Kubernetes support for orchestrated clusters. Vast.ai offers mostly bare-metal access to host GPUs, with host-varied storage (local SSDs, no managed NFS) and networking (1-100Gbps Ethernet/IB). Lacks native Kubernetes; users deploy via SSH/Docker. AWS emphasizes managed persistence/S3; Vast.ai prioritizes raw GPU passthrough.

Performance

AWS delivers consistent performance with validated multi-GPU scaling (e.g., P5's 3.6TB/s NVLink), high GPU utilization via Nitro, and DL benchmarks matching on-prem. Vast.ai matches single-node perf (A100/H100 FP16 >80% util) but varies 10-30% by host; multi-node scaling depends on host peering, often Ethernet-limited vs AWS IB. Availability stronger on AWS for premium GPUs; Vast.ai excels in consumer RTX for cheap inference.

Frequently Asked Questions

Which provider offers better spot instance pricing?▾

Both AWS and Vast.ai offer spot/preemptible instances, which can reduce costs by 50-80% compared to on-demand pricing. Spot instances are ideal for fault-tolerant workloads like batch inference, hyperparameter tuning, and distributed training with checkpointing. The actual savings depend on current demand and GPU availability, so we recommend comparing real-time spot prices for your specific GPU requirements on both platforms.

What is the minimum billing increment for each provider?▾

AWS bills per-second, while Vast.ai bills per-hour. Per-second billing from AWS offers better cost efficiency for short experiments and iterative development, as you only pay for exactly what you use.

Which provider has better compliance certifications for enterprise use?▾

AWS holds SOC 2, HIPAA, GDPR, ISO 27001 certifications. Vast.ai holds GDPR certification. For organizations with strict compliance requirements, AWS offers more comprehensive coverage.

Which provider offers better development tools like Jupyter notebooks?▾

Both AWS and Vast.ai offer built-in Jupyter notebook support, making it easy to start experimenting without additional setup. This is particularly valuable for data scientists and researchers who prefer interactive development environments. Additionally, both providers offer web-based terminal access for quick debugging.

Which provider has better Kubernetes support for orchestration?▾

AWS offers native Kubernetes support for container orchestration, while Vast.ai does not. If you're building production ML pipelines with Kubernetes-based tools like Kubeflow, Argo, or KServe, AWS will integrate more seamlessly with your workflow.

What is each provider best suited for?▾

AWS is best suited for Large-scale enterprises requiring deep integration with other cloud services; Organizations needing globally redundant availability zones. Vast.ai excels at Absolute lowest costs; Distributed experiments. Understanding these specializations helps you choose the provider that aligns with your primary use case, though both can handle a variety of GPU computing needs.

Which provider offers reserved instances for long-term savings?▾

AWS offers reserved instance pricing for long-term commitments, while Vast.ai does not currently offer this option. Reserved instances are ideal for predictable, steady-state workloads like always-on inference services. For variable workloads, on-demand or spot instances may offer better flexibility.

Which provider offers better enterprise support?▾

AWS offers dedicated enterprise support options, while Vast.ai may have more limited support tiers. Regarding SLAs: AWS offers SLA guarantees (99.99% uptime); Vast.ai has no published SLA.

Which provider has better API and automation support?▾

Both AWS and Vast.ai provide APIs for programmatic instance management, enabling automation of provisioning, scaling, and teardown operations. This is essential for integrating GPU resources into CI/CD pipelines and automated ML workflows.

Which provider has better container and Docker support?▾

Both AWS and Vast.ai support containerized workloads, allowing you to deploy Docker images with your ML frameworks, dependencies, and models pre-configured. This ensures reproducibility and simplifies deployment across development, staging, and production environments.

What unique features differentiate these providers?▾

AWS's standout features include: Proprietary silicon like Trainium and Inferentia chips; Fully managed ML development environment with SageMaker. Vast.ai's standout features include: Granular search filters like DLPerf/$; Decentralized marketplace. These differentiators may be decisive factors depending on your specific technical requirements and workflow preferences.

How do I get started with each provider?▾

To get started with AWS, visit their website at https://aws.amazon.com?utm_source=gpuperhour&utm_medium=referral to create an account and explore available GPU options. For Vast.ai, visit https://cloud.vast.ai/?ref_id=375842&utm_source=gpuperhour&utm_medium=referral to sign up. Both providers typically offer some form of free credits or trial period for new users. We recommend starting with a small experiment to evaluate the platform's ease of use, instance launch times, and overall fit for your workflow before committing to larger workloads.